Evidence map›Paper›PMID 41459181›Full record

ReviewEJIFCC2025

The Challenges of Data Privacy and Cybersecurity in Cloud Computing and Artificial Intelligence (AI) Applications for EQA Organizations.

Alexander Haliassos, Dimitrios Kasvis, Serafeim Karathanos

Abstract readReview
In one paragraph

Review in EJIFCC, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Alexander HaliassosESEAP & GSCC-CB, Athens, Greece.
Dimitrios KasvisHeadWay Consultants, Athens, Greece.
Serafeim KarathanosESEAP & GSCC-CB, Athens, Greece.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The adoption of cloud computing and Artificial Intelligence (AI) technologies offers significant advantages for External Quality Assessment (EQA) providers, including scalability, cost efficiency, and broader accessibility. However, these benefits come with substantial cybersecurity and data privacy challenges. Methodology: We performed a systematic literature review on cybersecurity risks in healthcare cloud computing, consulted experts in bioinformatics and cybersecurity, and analyzed real-world hacking incidents targeting EQA organizations. A risk-focused framework was developed to outline key challenges and best practice mitigation strategies. Results: Ten key challenges were identified: 1. data breaches and unauthorized access, 2. compliance with regulations such as HIPAA and GDPR, 3. data sovereignty and jurisdictional issues, 4. shared infrastructure vulnerabilities, 5. insider threats, 6. data loss and availability concerns, 7. inadequate security measures by cloud providers, 8. application vulnerabilities, 9. limited visibility and control, and 10. the complexity of cloud security management. Conclusion: To fully benefit from cloud computing and AI, EQA providers must implement robust security practices, ensure regulatory compliance, and continuously monitor their environments. Proactive cybersecurity strategies are essential to safeguarding sensitive laboratory data and maintaining operational continuity and accreditation.

Indexed as

AIartificial intelligenceCloud ComputingCybersecurityData PrivacyEQAPT

Identifiers

PMID41459181
PMCPMC12743334

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.